RandGen
Random number generator
Summary
This App generates pseudo-random numbers from various probability distributions and validates their fit and quality using Chi-Squared and Kolmogorov-Smirnov goodness-of-fit tests. This supports data-driven decision-making and enhances the understanding of system behavior by ensuring simulated data conforms to theoretical distributions.
Links
Tech Stack
| technologies | |
|---|---|
| backend | Flask Matplotlib SciPy NumPy Pandas |
| hosting | Render |
| frontend | HTML CSS Bootstrap Jinja2 |
| language | Python |
Features
- Generation of robust pseudo-random numbers for Uniform, Exponential, Normal, and Poisson distributions, essential for simulation models that include variability.
- Detailed parameter configuration for each distribution type, allowing precise simulation of specific scenarios.
- Implementation of the Chi-Squared Goodness-of-Fit Test, suitable for large samples (N>=30) and with automatic interval grouping to meet the minimum expected frequency requirement (>=5).
- Integration of the Kolmogorov-Smirnov Goodness-of-Fit Test, designed to validate data fit to continuous distributions, providing the D statistic and p-value.
- Ability to define a custom confidence level for both statistical tests, allowing the user to control the Type I error probability.
- Interactive visualization of frequency distribution through dynamic histograms, facilitating visual understanding of generated data.
- Export functionality for generated numbers to common formats such as CSV, Excel, and TXT, for further analysis or integration into other tools.
- Intuitive and adaptive user interface (responsive design) developed with Flask and Bootstrap, ensuring a smooth user experience across different devices.
Screenshots
